{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./mnist\\train-images-idx3-ubyte.gz\n",
      "Extracting ./mnist\\train-labels-idx1-ubyte.gz\n",
      "Extracting ./mnist\\t10k-images-idx3-ubyte.gz\n",
      "Extracting ./mnist\\t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "from tensorflow.examples.tutorials.mnist import mnist\n",
    "from minist import fullConnectedNet\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 调用系统提供的Mnist数据函数读入数据，如果没有下载的话则进行下载。\n",
    "data_dir = './mnist'\n",
    "data=input_data.read_data_sets(data_dir, one_hot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同优化算法的性能比较"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "step:2310,loss:0.3084695339202881,train accuracy:0.9505454301834106,test_accuracy:0.9433000087738037\n",
      "step:2340,loss:0.20999465882778168,train accuracy:0.944563627243042,test_accuracy:0.9387000203132629\n",
      "step:2370,loss:0.26520082354545593,train accuracy:0.9485090970993042,test_accuracy:0.9442999958992004\n",
      "step:2400,loss:0.2855631113052368,train accuracy:0.9413090944290161,test_accuracy:0.9333000183105469\n",
      "step:2430,loss:0.2343311607837677,train accuracy:0.9574363827705383,test_accuracy:0.9474999904632568\n",
      "step:2460,loss:0.23419751226902008,train accuracy:0.9451454281806946,test_accuracy:0.9369000196456909\n",
      "step:2490,loss:0.22309541702270508,train accuracy:0.9586363434791565,test_accuracy:0.9528999924659729\n",
      "step:2520,loss:0.2033749669790268,train accuracy:0.9538545608520508,test_accuracy:0.9469000101089478\n",
      "step:2550,loss:0.22995050251483917,train accuracy:0.9621090888977051,test_accuracy:0.954200029373169\n",
      "step:2580,loss:0.1695822775363922,train accuracy:0.9658908843994141,test_accuracy:0.9613000154495239\n",
      "step:2610,loss:0.3621678054332733,train accuracy:0.9404545426368713,test_accuracy:0.9388999938964844\n",
      "step:2640,loss:0.38421428203582764,train accuracy:0.9513999819755554,test_accuracy:0.9437999725341797\n",
      "step:2670,loss:0.26663026213645935,train accuracy:0.9524909257888794,test_accuracy:0.9453999996185303\n",
      "step:2700,loss:0.19337838888168335,train accuracy:0.9580545425415039,test_accuracy:0.9484000205993652\n",
      "step:2730,loss:0.2757055163383484,train accuracy:0.939054548740387,test_accuracy:0.9327999949455261\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:2760,loss:0.3227824866771698,train accuracy:0.9485454559326172,test_accuracy:0.9383000135421753\n",
      "step:2790,loss:0.6004922389984131,train accuracy:0.9420727491378784,test_accuracy:0.9366000294685364\n",
      "step:2820,loss:0.42393848299980164,train accuracy:0.9280181527137756,test_accuracy:0.9194999933242798\n",
      "step:2850,loss:0.29718583822250366,train accuracy:0.9439091086387634,test_accuracy:0.9366000294685364\n",
      "step:2880,loss:0.2244441956281662,train accuracy:0.9628727436065674,test_accuracy:0.9571999907493591\n",
      "step:2910,loss:0.29035601019859314,train accuracy:0.9620000123977661,test_accuracy:0.9537000060081482\n",
      "step:2940,loss:0.21113285422325134,train accuracy:0.9579818248748779,test_accuracy:0.9509999752044678\n",
      "step:2970,loss:0.4209345281124115,train accuracy:0.9249091148376465,test_accuracy:0.9185000061988831\n",
      "step:3000,loss:0.2586149275302887,train accuracy:0.9362000226974487,test_accuracy:0.9276000261306763\n"
     ]
    }
   ],
   "source": [
    "param={\"learningRate\":0.02,\"momentum\":0.01}\n",
    "active_func=\"relu\"\n",
    "\n",
    "tr_loss={}\n",
    "tr_acc={}\n",
    "te_acc={}\n",
    "for i,solver in enumerate([\"GD\",\"Moment\",\"Adagrad\",\"adam\",\"RMSProp\"]):\n",
    "    n=fullConnectedNet(data,[100,10],active_func,solver,param)\n",
    "    tr_loss[i],tr_acc[i],te_acc[i]=n.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SuqQvnmBX7w52hVbTm2rBqjlxmNxYs+3c+1KMhB4joUfRRZgkQRKyD103kUyZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f0e5e99978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 显示结果\n",
    "for i,solver in enumerate([\"GD\",\"Moment\",\"Adagrad\",\"adam\",\"RMSProp\"]):\n",
    "    plt.plot(range(1,len(te_acc[i])+1),te_acc[i], label = solver)\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9659\n"
     ]
    }
   ],
   "source": [
    "print(max(te_acc[3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同隐层数目的比较"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:30,loss:0.4120636284351349,train accuracy:0.8765272498130798,test_accuracy:0.8827999830245972\n",
      "step:60,loss:0.30058541893959045,train accuracy:0.9083454608917236,test_accuracy:0.9139000177383423\n",
      "step:90,loss:0.24525944888591766,train accuracy:0.9269999861717224,test_accuracy:0.9337999820709229\n",
      "step:120,loss:0.2638597786426544,train accuracy:0.9186182022094727,test_accuracy:0.9172000288963318\n",
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      "step:2970,loss:0.10813551396131516,train accuracy:0.9715999960899353,test_accuracy:0.9585000276565552\n",
      "step:3000,loss:0.08182236552238464,train accuracy:0.975600004196167,test_accuracy:0.9620000123977661\n",
      "accuracy when neuron number 6:700\n"
     ]
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    {
     "data": {
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8uo2D40H6NO1YiGDSWjEvaQBAs/Ij6OWDmLHhPHziQqt+V4Q7n9wFwMcvW8/qcj1F4wPo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\nK6VUJ/L/AUI//zQTJNE+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2675d89e668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "param={\"learningRate\":0.02,\"momentum\":0.01}\n",
    "active_func=\"relu\"\n",
    "solver=\"adam\"\n",
    "\n",
    "tr_loss={}\n",
    "tr_acc={}\n",
    "te_acc={}\n",
    "for i,num in enumerate([100,200,300,400,500,600,700]):\n",
    "    n=fullConnectedNet(data,[num,10],active_func,solver,param)\n",
    "    tr_loss[i],tr_acc[i],te_acc[i]=n.run()\n",
    "    plt.plot(range(1,len(te_acc[i])+1),te_acc[i], label = \"hidden1_neuron_{}\".format(num))\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy when neuron number is 100:0.9646000266075134\n",
      "accuracy when neuron number is 200:0.9631999731063843\n",
      "accuracy when neuron number is 300:0.963100016117096\n",
      "accuracy when neuron number is 400:0.9634000062942505\n",
      "accuracy when neuron number is 500:0.9642999768257141\n",
      "accuracy when neuron number is 600:0.9659000039100647\n",
      "accuracy when neuron number is 700:0.9638000130653381\n"
     ]
    },
    {
     "data": {
      "image/png": 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1y0gbS5B7dxAZPFZ8WFpHeVjDuYeKGPnLJmRZ5pobr2TNohSLdL2cNb8HnZTD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NdZTGoUnO7EHvVWSh1/mqefWLYp+tGKCgAcCOXecgiBS2+KrlSHp3n6iWWbqihuD9HzzyCKkJHe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\nK6VUJ/L/AUI//zQTJNE+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26700fea908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i,num in enumerate([100,200,300,400,500,600,700]):\n",
    "    print(\"accuracy when neuron number is {}:{}\".format(num,max(te_acc[i])))\n",
    "    plt.plot(range(1,len(te_acc[i])+1),te_acc[i], label = \"hidden1_neuron_{}\".format(num))\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同激活函数的比较"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:30,loss:0.4202669858932495,train accuracy:0.8799636363983154,test_accuracy:0.8827999830245972\n",
      "step:60,loss:0.31397274136543274,train accuracy:0.9042545557022095,test_accuracy:0.9038000106811523\n",
      "step:90,loss:0.3164460361003876,train accuracy:0.8985090851783752,test_accuracy:0.8978999853134155\n",
      "step:120,loss:0.2423429936170578,train accuracy:0.9259636402130127,test_accuracy:0.926800012588501\n",
      "step:150,loss:0.22220584750175476,train accuracy:0.9311636090278625,test_accuracy:0.9276000261306763\n",
      "step:180,loss:0.2089654803276062,train accuracy:0.9356727004051208,test_accuracy:0.9343000054359436\n",
      "step:210,loss:0.21653003990650177,train accuracy:0.9318727254867554,test_accuracy:0.9254000186920166\n",
      "step:240,loss:0.2302992343902588,train accuracy:0.926836371421814,test_accuracy:0.9266999959945679\n",
      "step:270,loss:0.24213358759880066,train accuracy:0.9285818338394165,test_accuracy:0.927299976348877\n",
      "step:300,loss:0.19322936236858368,train accuracy:0.9427090883255005,test_accuracy:0.9390000104904175\n",
      "step:330,loss:0.18247090280056,train accuracy:0.9431999921798706,test_accuracy:0.9383000135421753\n",
      "step:360,loss:0.18689808249473572,train accuracy:0.9415454268455505,test_accuracy:0.9391000270843506\n",
      "step:390,loss:0.16911230981349945,train accuracy:0.9502182006835938,test_accuracy:0.945900022983551\n",
      "step:420,loss:0.1809305101633072,train accuracy:0.9454181790351868,test_accuracy:0.9372000098228455\n",
      "step:450,loss:0.15738970041275024,train accuracy:0.9525454640388489,test_accuracy:0.9453999996185303\n",
      "step:480,loss:0.1800241768360138,train accuracy:0.9470909237861633,test_accuracy:0.9438999891281128\n",
      "step:510,loss:0.174920916557312,train accuracy:0.9499272704124451,test_accuracy:0.9463000297546387\n",
      "step:540,loss:0.16024643182754517,train accuracy:0.9523636102676392,test_accuracy:0.9448999762535095\n",
      "step:570,loss:0.14976944029331207,train accuracy:0.953709065914154,test_accuracy:0.9462000131607056\n",
      "step:600,loss:0.14931620657444,train accuracy:0.9569091200828552,test_accuracy:0.9491999745368958\n",
      "step:630,loss:0.1720859557390213,train accuracy:0.9480181932449341,test_accuracy:0.9386000037193298\n",
      "step:660,loss:0.1451817899942398,train accuracy:0.9569454789161682,test_accuracy:0.954200029373169\n",
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      "step:720,loss:0.1593482941389084,train accuracy:0.9513454437255859,test_accuracy:0.947700023651123\n",
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   ],
   "source": [
    "param={\"learningRate\":0.02,\"momentum\":0.01}\n",
    "solver=\"adam\"\n",
    "\n",
    "tr_loss={}\n",
    "tr_acc={}\n",
    "te_acc={}\n",
    "for i,active_func in enumerate([\"relu\",\"relu6\",\"softplus\",\"sigmod\",\"tanh\"]):\n",
    "    n=fullConnectedNet(data,[100,10],active_func,solver,param)\n",
    "    tr_loss[i],tr_acc[i],te_acc[i]=n.run()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy when activation function is relu:0.9620000123977661\n",
      "accuracy when activation function is relu6:0.9599000215530396\n",
      "accuracy when activation function is softplus:0.963699996471405\n",
      "accuracy when activation function is sigmod:0.967199981212616\n",
      "accuracy when activation function is tanh:0.9537000060081482\n"
     ]
    },
    {
     "data": {
      "image/png": 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ldm9/jLyzSLLru6qrEZ1Of4ZsYaNURTG3uh2bS7oHyqQkEp96Em9LK/KoKMIm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\nF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19c9243dd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i,active_func in enumerate([\"relu\",\"relu6\",\"softplus\",\"sigmod\",\"tanh\"]):\n",
    "    plt.plot(range(1,len(te_acc[i])+1),te_acc[i], label = active_func)\n",
    "    print(\"accuracy when activation function is {}:{}\".format(active_func,max(te_acc[i])))\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 不同学习率的比较"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 粗调"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:30,loss:0.735953152179718,train accuracy:0.831854522228241,test_accuracy:0.8374000191688538\n",
      "step:60,loss:0.4674457311630249,train accuracy:0.8757818341255188,test_accuracy:0.8842999935150146\n",
      "step:90,loss:0.3749382197856903,train accuracy:0.8978545665740967,test_accuracy:0.9045000076293945\n",
      "step:120,loss:0.33332720398902893,train accuracy:0.9064363837242126,test_accuracy:0.9111999869346619\n",
      "step:150,loss:0.3111853003501892,train accuracy:0.9124363660812378,test_accuracy:0.9146000146865845\n",
      "step:180,loss:0.29249995946884155,train accuracy:0.9196181893348694,test_accuracy:0.9203000068664551\n",
      "step:210,loss:0.28663429617881775,train accuracy:0.9161999821662903,test_accuracy:0.9190000295639038\n",
      "step:240,loss:0.27530959248542786,train accuracy:0.9198545217514038,test_accuracy:0.9193000197410583\n",
      "step:270,loss:0.24424158036708832,train accuracy:0.9298363924026489,test_accuracy:0.9284999966621399\n",
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      "step:2640,loss:0.29077160358428955,train accuracy:0.9184181690216064,test_accuracy:0.9186000227928162\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:2670,loss:0.2606451213359833,train accuracy:0.9255090951919556,test_accuracy:0.9251999855041504\n",
      "step:2700,loss:0.28115108609199524,train accuracy:0.920236349105835,test_accuracy:0.9208999872207642\n",
      "step:2730,loss:0.28764620423316956,train accuracy:0.918145477771759,test_accuracy:0.9172999858856201\n",
      "step:2760,loss:0.265871524810791,train accuracy:0.9240182042121887,test_accuracy:0.9232000112533569\n",
      "step:2790,loss:0.3078818619251251,train accuracy:0.9062908887863159,test_accuracy:0.906499981880188\n",
      "step:2820,loss:0.2513718008995056,train accuracy:0.9286909103393555,test_accuracy:0.9258000254631042\n",
      "step:2850,loss:0.25672534108161926,train accuracy:0.9282363653182983,test_accuracy:0.9298999905586243\n",
      "step:2880,loss:0.252944678068161,train accuracy:0.9294909238815308,test_accuracy:0.9282000064849854\n",
      "step:2910,loss:0.25305604934692383,train accuracy:0.9271454811096191,test_accuracy:0.9266999959945679\n",
      "step:2940,loss:0.30350354313850403,train accuracy:0.904272735118866,test_accuracy:0.8985000252723694\n",
      "step:2970,loss:0.26659566164016724,train accuracy:0.9226182103157043,test_accuracy:0.9205999970436096\n",
      "step:3000,loss:0.2595590651035309,train accuracy:0.9258363842964172,test_accuracy:0.9276999831199646\n"
     ]
    }
   ],
   "source": [
    "param={\"learningRate\":0.02,\"momentum\":0.01}\n",
    "solver=\"adam\"\n",
    "active_func=\"sigmod\"\n",
    "\n",
    "search=[0.005,0.01,0.02,0.05,0.1]\n",
    "tr_loss={}\n",
    "tr_acc={}\n",
    "te_acc={}\n",
    "for i,lr in enumerate(search):\n",
    "    param[\"learningRate\"]=lr\n",
    "    n=fullConnectedNet(data,[100,10],active_func,solver,param)\n",
    "    tr_loss[i],tr_acc[i],te_acc[i]=n.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy when learning rate is 0.005:0.973800003528595\n",
      "accuracy when learning rate is 0.01:0.9729999899864197\n",
      "accuracy when learning rate is 0.02:0.9688000082969666\n",
      "accuracy when learning rate is 0.05:0.9514999985694885\n",
      "accuracy when learning rate is 0.1:0.9298999905586243\n"
     ]
    },
    {
     "data": {
      "image/png": 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4xsL+tele2xVRFDGKRu5EpfH57gCuhyUxpmU5xrYsh5layaOEDL72uc3+m1FUcLakeQVH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\nBARG1xyd6zL6+Z5b+AbF554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69d/kLRTAqNUSt2gRmmrVCnh6FIYgCJhVr4ZZ9Wo4fTSZ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\naxNLoWfnxOwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a97e7fdbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i,lr in enumerate(search):\n",
    "    plt.plot(range(1,len(te_acc[i])+1),te_acc[i], label = lr)\n",
    "    print(\"accuracy when learning rate is {}:{}\".format(lr,max(te_acc[i])))\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 细调"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:30,loss:1.0668119192123413,train accuracy:0.7927636504173279,test_accuracy:0.804099977016449\n",
      "step:60,loss:0.6568924188613892,train accuracy:0.8515272736549377,test_accuracy:0.8650000095367432\n",
      "step:90,loss:0.4979274272918701,train accuracy:0.876836359500885,test_accuracy:0.8863000273704529\n",
      "step:120,loss:0.4259701371192932,train accuracy:0.8852909207344055,test_accuracy:0.8907999992370605\n",
      "step:150,loss:0.3782140016555786,train accuracy:0.8997818231582642,test_accuracy:0.9035999774932861\n",
      "step:180,loss:0.35011494159698486,train accuracy:0.9063636660575867,test_accuracy:0.9077000021934509\n",
      "step:210,loss:0.33091768622398376,train accuracy:0.9084727168083191,test_accuracy:0.9110000133514404\n",
      "step:240,loss:0.30510711669921875,train accuracy:0.9158545732498169,test_accuracy:0.9168000221252441\n",
      "step:270,loss:0.29243630170822144,train accuracy:0.918363630771637,test_accuracy:0.9172999858856201\n",
      "step:300,loss:0.28094106912612915,train accuracy:0.9204909205436707,test_accuracy:0.921500027179718\n",
      "step:330,loss:0.2715528905391693,train accuracy:0.9247090816497803,test_accuracy:0.9265000224113464\n",
      "step:360,loss:0.2612149119377136,train accuracy:0.9257636070251465,test_accuracy:0.9240999817848206\n",
      "step:390,loss:0.25544053316116333,train accuracy:0.9284181594848633,test_accuracy:0.9284999966621399\n",
      "step:420,loss:0.24660903215408325,train accuracy:0.9292545318603516,test_accuracy:0.9282000064849854\n",
      "step:450,loss:0.240677610039711,train accuracy:0.93058180809021,test_accuracy:0.9319000244140625\n",
      "step:480,loss:0.22753582894802094,train accuracy:0.9352181553840637,test_accuracy:0.9340000152587891\n",
      "step:510,loss:0.22044283151626587,train accuracy:0.9370909333229065,test_accuracy:0.935699999332428\n",
      "step:540,loss:0.21859171986579895,train accuracy:0.9377090930938721,test_accuracy:0.9390000104904175\n",
      "step:570,loss:0.21628828346729279,train accuracy:0.9382181763648987,test_accuracy:0.9387000203132629\n",
      "step:600,loss:0.20479100942611694,train accuracy:0.9430545568466187,test_accuracy:0.9415000081062317\n",
      "step:630,loss:0.1999763548374176,train accuracy:0.9430545568466187,test_accuracy:0.9409000277519226\n",
      "step:660,loss:0.19535931944847107,train accuracy:0.9446908831596375,test_accuracy:0.9426000118255615\n",
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      "step:2880,loss:0.04178709164261818,train accuracy:0.9885818362236023,test_accuracy:0.972599983215332\n",
      "step:2910,loss:0.03812391683459282,train accuracy:0.9896000027656555,test_accuracy:0.9739000201225281\n",
      "step:2940,loss:0.0381927452981472,train accuracy:0.9895636439323425,test_accuracy:0.9745000004768372\n",
      "step:2970,loss:0.04070258513092995,train accuracy:0.9892363548278809,test_accuracy:0.974399983882904\n",
      "step:3000,loss:0.037301432341337204,train accuracy:0.9900000095367432,test_accuracy:0.972599983215332\n"
     ]
    }
   ],
   "source": [
    "param={\"learningRate\":0.02,\"momentum\":0.01}\n",
    "solver=\"adam\"\n",
    "active_func=\"sigmod\"\n",
    "\n",
    "search=[0.003,0.004,0.005,0.006]\n",
    "tr_loss={}\n",
    "tr_acc={}\n",
    "te_acc={}\n",
    "for i,lr in enumerate(search):\n",
    "    param[\"learningRate\"]=lr\n",
    "    n=fullConnectedNet(data,[100,10],active_func,solver,param)\n",
    "    tr_loss[i],tr_acc[i],te_acc[i]=n.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy when learning rate is 0.003:0.9715999960899353\n",
      "accuracy when learning rate is 0.004:0.9735000133514404\n",
      "accuracy when learning rate is 0.005:0.9743000268936157\n",
      "accuracy when learning rate is 0.006:0.9757000207901001\n"
     ]
    },
    {
     "data": {
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wx4VReFN/QgcNpVtU9xMzUpzufhM4vBoKdwES4nq6B0hrdrE7jcPqriwObA6n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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19c96b30ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i,lr in enumerate(search):\n",
    "    plt.plot(range(1,len(te_acc[i])+1),te_acc[i], label = lr)\n",
    "    print(\"accuracy when learning rate is {}:{}\".format(lr,max(te_acc[i])))\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 再调一下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:30,loss:0.7167858481407166,train accuracy:0.8107454776763916,test_accuracy:0.8155999779701233\n",
      "step:60,loss:0.4376247823238373,train accuracy:0.8791090846061707,test_accuracy:0.8860999941825867\n",
      "step:90,loss:0.35799288749694824,train accuracy:0.9009636640548706,test_accuracy:0.9082000255584717\n",
      "step:120,loss:0.31490248441696167,train accuracy:0.9120727181434631,test_accuracy:0.9146000146865845\n",
      "step:150,loss:0.3005043566226959,train accuracy:0.9114000201225281,test_accuracy:0.9158999919891357\n",
      "step:180,loss:0.2888356149196625,train accuracy:0.9212727546691895,test_accuracy:0.9221000075340271\n",
      "step:210,loss:0.25474390387535095,train accuracy:0.926727294921875,test_accuracy:0.9264000058174133\n",
      "step:240,loss:0.24343952536582947,train accuracy:0.9294000267982483,test_accuracy:0.930899977684021\n",
      "step:270,loss:0.2318776696920395,train accuracy:0.9339818358421326,test_accuracy:0.9333000183105469\n",
      "step:300,loss:0.21684657037258148,train accuracy:0.9374181628227234,test_accuracy:0.9370999932289124\n",
      "step:330,loss:0.2108711302280426,train accuracy:0.9387999773025513,test_accuracy:0.9413999915122986\n",
      "step:360,loss:0.1963304728269577,train accuracy:0.9434909224510193,test_accuracy:0.9427000284194946\n",
      "step:390,loss:0.18882453441619873,train accuracy:0.9448545575141907,test_accuracy:0.942799985408783\n",
      "step:420,loss:0.18725138902664185,train accuracy:0.94643634557724,test_accuracy:0.9465000033378601\n",
      "step:450,loss:0.17809230089187622,train accuracy:0.9491272568702698,test_accuracy:0.9455000162124634\n",
      "step:480,loss:0.16839805245399475,train accuracy:0.9525636434555054,test_accuracy:0.9509999752044678\n",
      "step:510,loss:0.16324979066848755,train accuracy:0.9552000164985657,test_accuracy:0.9516000151634216\n",
      "step:540,loss:0.15918120741844177,train accuracy:0.9547091126441956,test_accuracy:0.9524999856948853\n",
      "step:570,loss:0.16291958093643188,train accuracy:0.9522908926010132,test_accuracy:0.9484999775886536\n",
      "step:600,loss:0.15581735968589783,train accuracy:0.9534000158309937,test_accuracy:0.9484000205993652\n",
      "step:630,loss:0.15222713351249695,train accuracy:0.9559272527694702,test_accuracy:0.9506999850273132\n",
      "step:660,loss:0.14629915356636047,train accuracy:0.9573454260826111,test_accuracy:0.9506000280380249\n",
      "step:690,loss:0.14400139451026917,train accuracy:0.957909107208252,test_accuracy:0.9509000182151794\n",
      "step:720,loss:0.14659392833709717,train accuracy:0.956636369228363,test_accuracy:0.9524999856948853\n",
      "step:750,loss:0.1325131058692932,train accuracy:0.961472749710083,test_accuracy:0.9573000073432922\n",
      "step:780,loss:0.13192574679851532,train accuracy:0.9623818397521973,test_accuracy:0.95660001039505\n",
      "step:810,loss:0.12518954277038574,train accuracy:0.9636181592941284,test_accuracy:0.9606999754905701\n",
      "step:840,loss:0.12514357268810272,train accuracy:0.9643272757530212,test_accuracy:0.9596999883651733\n",
      "step:870,loss:0.11933048814535141,train accuracy:0.9666545391082764,test_accuracy:0.9603999853134155\n",
      "step:900,loss:0.11804661154747009,train accuracy:0.9662363529205322,test_accuracy:0.9577999711036682\n",
      "step:930,loss:0.10948039591312408,train accuracy:0.968999981880188,test_accuracy:0.9621999859809875\n",
      "step:960,loss:0.11029408872127533,train accuracy:0.9675454497337341,test_accuracy:0.9617999792098999\n",
      "step:990,loss:0.10696057975292206,train accuracy:0.9691091179847717,test_accuracy:0.9624999761581421\n",
      "step:1020,loss:0.10705959051847458,train accuracy:0.968890905380249,test_accuracy:0.9617000222206116\n",
      "step:1050,loss:0.10743138939142227,train accuracy:0.9689454436302185,test_accuracy:0.961899995803833\n",
      "step:1080,loss:0.10546240955591202,train accuracy:0.9702727198600769,test_accuracy:0.9609000086784363\n",
      "step:1110,loss:0.1034981980919838,train accuracy:0.9702363610267639,test_accuracy:0.961899995803833\n",
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     ]
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   ],
   "source": [
    "param={\"learningRate\":0.02,\"momentum\":0.01}\n",
    "solver=\"adam\"\n",
    "active_func=\"sigmod\"\n",
    "\n",
    "search=[0.006,0.007,0.008,0.009,0.01,0.012]\n",
    "tr_loss={}\n",
    "tr_acc={}\n",
    "te_acc={}\n",
    "for i,lr in enumerate(search):\n",
    "    param[\"learningRate\"]=lr\n",
    "    n=fullConnectedNet(data,[100,10],active_func,solver,param)\n",
    "    tr_loss[i],tr_acc[i],te_acc[i]=n.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy when learning rate is 0.006:0.9732000231742859\n",
      "accuracy when learning rate is 0.007:0.9739000201225281\n",
      "accuracy when learning rate is 0.008:0.9750999808311462\n",
      "accuracy when learning rate is 0.009:0.9746999740600586\n",
      "accuracy when learning rate is 0.01:0.9745000004768372\n",
      "accuracy when learning rate is 0.012:0.973800003528595\n"
     ]
    },
    {
     "data": {
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0hu+6KJosTpweL7vffIGSnN1c/tcnee1PtzD+2psYePF0hBBYXVYONR5iYNTA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\n5UVrZYCdO3cCsG3bNp555hlqa2vTHnsqM/1JwBZ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19c9c798ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i,lr in enumerate(search):\n",
    "    plt.plot(range(1,len(te_acc[i])+1),te_acc[i], label = lr)\n",
    "    print(\"accuracy when learning rate is {}:{}\".format(lr,max(te_acc[i])))\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 学习率衰减的比较"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:30,loss:0.5160551071166992,train accuracy:0.8563454747200012,test_accuracy:0.867900013923645\n",
      "step:60,loss:0.37261679768562317,train accuracy:0.8918363451957703,test_accuracy:0.9002000093460083\n",
      "step:90,loss:0.3097778260707855,train accuracy:0.9087454676628113,test_accuracy:0.9110000133514404\n",
      "step:120,loss:0.289936900138855,train accuracy:0.9176545739173889,test_accuracy:0.9176999926567078\n",
      "step:150,loss:0.26011642813682556,train accuracy:0.9228000044822693,test_accuracy:0.9258999824523926\n",
      "step:180,loss:0.2506985366344452,train accuracy:0.926727294921875,test_accuracy:0.9273999929428101\n",
      "step:210,loss:0.233974888920784,train accuracy:0.9332908987998962,test_accuracy:0.9330000281333923\n",
      "step:240,loss:0.2086987942457199,train accuracy:0.9398545622825623,test_accuracy:0.9376999735832214\n",
      "step:270,loss:0.19977602362632751,train accuracy:0.9412727355957031,test_accuracy:0.9380999803543091\n",
      "step:300,loss:0.19804596900939941,train accuracy:0.9408363699913025,test_accuracy:0.9373000264167786\n",
      "step:330,loss:0.16953817009925842,train accuracy:0.951090931892395,test_accuracy:0.9449999928474426\n",
      "step:360,loss:0.1699707955121994,train accuracy:0.9510363340377808,test_accuracy:0.947700023651123\n",
      "step:390,loss:0.16597577929496765,train accuracy:0.9518181681632996,test_accuracy:0.9485999941825867\n",
      "step:420,loss:0.15831609070301056,train accuracy:0.953745424747467,test_accuracy:0.949400007724762\n",
      "step:450,loss:0.15100997686386108,train accuracy:0.9558908939361572,test_accuracy:0.9521999955177307\n",
      "step:480,loss:0.15257269144058228,train accuracy:0.9571272730827332,test_accuracy:0.9535999894142151\n",
      "step:510,loss:0.14436694979667664,train accuracy:0.958581805229187,test_accuracy:0.9520999789237976\n",
      "step:540,loss:0.14450326561927795,train accuracy:0.9588000178337097,test_accuracy:0.9549000263214111\n",
      "step:570,loss:0.13991358876228333,train accuracy:0.9599636197090149,test_accuracy:0.9544000029563904\n",
      "step:600,loss:0.13245923817157745,train accuracy:0.962181806564331,test_accuracy:0.9557999968528748\n",
      "step:630,loss:0.1300477385520935,train accuracy:0.9622727036476135,test_accuracy:0.9560999870300293\n",
      "step:660,loss:0.1275569349527359,train accuracy:0.9638909101486206,test_accuracy:0.9570000171661377\n",
      "step:690,loss:0.12454254180192947,train accuracy:0.9649454355239868,test_accuracy:0.9574999809265137\n",
      "step:720,loss:0.12133724987506866,train accuracy:0.9647636413574219,test_accuracy:0.9588000178337097\n",
      "step:750,loss:0.12015494704246521,train accuracy:0.9658181667327881,test_accuracy:0.9591000080108643\n",
      "step:780,loss:0.11946503818035126,train accuracy:0.9662363529205322,test_accuracy:0.9587000012397766\n",
      "step:810,loss:0.11922595649957657,train accuracy:0.9672909379005432,test_accuracy:0.9599999785423279\n",
      "step:840,loss:0.11494074761867523,train accuracy:0.9679090976715088,test_accuracy:0.9610999822616577\n",
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    {
     "name": "stdout",
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     "text": [
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      "step:2820,loss:0.20563891530036926,train accuracy:0.9384727478027344,test_accuracy:0.933899998664856\n",
      "step:2850,loss:0.2011161595582962,train accuracy:0.9417272806167603,test_accuracy:0.9355999827384949\n",
      "step:2880,loss:0.2142419070005417,train accuracy:0.9381272792816162,test_accuracy:0.9368000030517578\n",
      "step:2910,loss:0.19501185417175293,train accuracy:0.9422727227210999,test_accuracy:0.9412999749183655\n",
      "step:2940,loss:0.1922842264175415,train accuracy:0.9434909224510193,test_accuracy:0.940500020980835\n",
      "step:2970,loss:0.19498269259929657,train accuracy:0.940890908241272,test_accuracy:0.9381999969482422\n",
      "step:3000,loss:0.18938079476356506,train accuracy:0.943363606929779,test_accuracy:0.9375\n"
     ]
    }
   ],
   "source": [
    "param={\"learningRate\":0.01,\"learningRateDecay\":0.5,\"decayStep\":1000}\n",
    "solver=\"adam\"\n",
    "active_func=\"sigmod\"\n",
    "\n",
    "search_step=[300,500,1000,1500]\n",
    "search_lr=[0.01,0.03,0.05,0.07,0.1]\n",
    "tr_loss={}\n",
    "tr_acc={}\n",
    "te_acc={}\n",
    "for i,step in enumerate(search_step):\n",
    "    for i,lr in enumerate(search_lr):\n",
    "        param[\"decayStep\"]=step\n",
    "        param[\"learningRate\"]=lr\n",
    "        n=fullConnectedNet(data,[100,10],active_func,solver,param)\n",
    "        tr_loss[i],tr_acc[i],te_acc[i]=n.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy when step is 300, learning rate is 0.01:0.9764999747276306\n",
      "accuracy when step is 500, learning rate is 0.01:0.9764999747276306\n",
      "accuracy when step is 1000, learning rate is 0.01:0.9764999747276306\n",
      "accuracy when step is 1500, learning rate is 0.01:0.9764999747276306\n"
     ]
    },
    {
     "data": {
      "image/png": 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n9Pl29sucWJhATFJxabnc38OFE306M+rpGaVPJCrvwM6N/Pr03fjmWSho7o2npYQuu4tL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\nRDTpK6VUI/L/CMJcCKvoRN0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e7b7a2aeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i,step in enumerate(search_step):\n",
    "    for i,lr in enumerate(search_lr):\n",
    "        max_acc=max(te_acc[i])\n",
    "        if max_acc>0.97:\n",
    "            plt.plot(range(1,len(te_acc[i])+1),te_acc[i], label = \"{}_{}\".format(step,lr))\n",
    "            print(\"accuracy when step is {}, learning rate is {}:{}\".format(step,lr,max_acc))\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 正则的比较"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "step:30,loss:0.5259886980056763,train accuracy:0.8647090792655945,test_accuracy:0.8734999895095825\n",
      "step:60,loss:0.36591270565986633,train accuracy:0.8955636620521545,test_accuracy:0.9004999995231628\n",
      "step:90,loss:0.31621286273002625,train accuracy:0.9108363389968872,test_accuracy:0.9139000177383423\n",
      "step:120,loss:0.29095736145973206,train accuracy:0.9149818420410156,test_accuracy:0.9168999791145325\n",
      "step:150,loss:0.25898537039756775,train accuracy:0.9247090816497803,test_accuracy:0.9253000020980835\n",
      "step:180,loss:0.2423708289861679,train accuracy:0.9298545718193054,test_accuracy:0.9304999709129333\n",
      "step:210,loss:0.23476949334144592,train accuracy:0.9326545596122742,test_accuracy:0.9298999905586243\n",
      "step:240,loss:0.2225106656551361,train accuracy:0.9362909197807312,test_accuracy:0.932200014591217\n",
      "step:270,loss:0.19562961161136627,train accuracy:0.9443454742431641,test_accuracy:0.9419999718666077\n",
      "step:300,loss:0.18331278860569,train accuracy:0.9480727314949036,test_accuracy:0.9455000162124634\n",
      "step:330,loss:0.1914566457271576,train accuracy:0.9422727227210999,test_accuracy:0.9372000098228455\n",
      "step:360,loss:0.19456090033054352,train accuracy:0.9415818452835083,test_accuracy:0.9379000067710876\n",
      "step:390,loss:0.1579170525074005,train accuracy:0.9551636576652527,test_accuracy:0.9483000040054321\n",
      "step:420,loss:0.1497224122285843,train accuracy:0.9573454260826111,test_accuracy:0.9509999752044678\n",
      "step:450,loss:0.14353397488594055,train accuracy:0.9582181572914124,test_accuracy:0.954800009727478\n",
      "step:480,loss:0.14454448223114014,train accuracy:0.958581805229187,test_accuracy:0.9534000158309937\n",
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      "step:540,loss:0.12910063564777374,train accuracy:0.963527262210846,test_accuracy:0.9578999876976013\n",
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     ]
    }
   ],
   "source": [
    "param={\"learningRate\":0.01,\"learningRateDecay\":0.5,\"decayStep\":1500}\n",
    "solver=\"adam\"\n",
    "active_func=\"sigmod\"\n",
    "\n",
    "n=fullConnectedNet(data,[100,10],active_func,solver,param)\n",
    "tr_loss,tr_acc,te_acc=n.run(30000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型优化Hint：\n",
    "- 多隐层\n",
    "- 激活函数\n",
    "- 正则化\n",
    "- 初始化\n",
    "- 摸索一下各个超参数\n",
    "  - 隐层神经元数量\n",
    "  - 学习率\n",
    "  - 正则化惩罚因子\n",
    "  - 最好每隔几个step就对loss、accuracy等等进行一次输出，这样才能有根据地进行调整"
   ]
  }
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